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3.4.5. Windowing Functions
💡 First Principle: Windowing functions divide continuous streams into finite chunks for aggregation—because you can't summarize infinity. Different window types serve different analytical needs: counting events per hour, calculating rolling averages, or grouping activity sessions.
Scenario: You need to: (1) Count events every 5 minutes (tumbling), (2) Calculate rolling average over overlapping intervals (hopping), (3) Group events until no activity for 30 seconds (session).
Window Types
| Window Type | Behavior | Use Case |
|---|---|---|
| Tumbling | Fixed, non-overlapping intervals | Hourly counts, daily summaries |
| Hopping | Fixed intervals with overlap | Rolling averages |
| Sliding | Event-triggered, fixed length | Per-event rolling calculations |
| Session | Gap-based grouping | User activity sessions |
Visual: Window Types
Written byAlvin Varughese
Founder•15 professional certifications